Fundamental Python Data Science Libraries: A Cheatsheet (Part 2/4)

If you are a developer and want to integrate data manipulation or science into your product or starting your journey in data science, here are the Python libraries you need to know.

1. NumPy
2. Pandas
3. Matplotlib
4. Scikit-Learn

The goal of this series is to provide introductions, highlights, and demonstrations of how to use the must-have libraries so you can pick what to explore more in depth.

pandas

This library is built on top of NumPy, which you may remember from my last article. Pandas takes NumPy’s powerful mathematical array-magic one step further. It allows you to store & manipulate data in a relational table structure.

Focus of the Library

This library focuses on two objects: the Series (1D) and the DataFrame (2D). Each allow you to set:

• an index — that lets you find and manipulate certain rows
• column names — that lets you find and manipulate certain columns

Having SQL deja-vu yet?

Installation

Open a command line and type in

pip install pandas

Windows: in the past I have found installing NumPy & other scientific packages to be a headache, so I encourage all you Windows users to download Anaconda’s distribution of Python which already comes with all the mathematical and scientific libraries installed.

Details

A pandas data structure differs from a NumPy array in a couple of ways:

1. All data in a NumPy array must be of the same data type, a pandas data structure can hold multiple data types
2. A pandas data structure allows you to name rows and columns
3. NumPy arrays can reach multiple dimensions, pandas data structures limit you to just 1 & 2D.*

*there is a 3D pandas data structure called a Panel but it is depreciated

Let’s dive in!

import pandas as pdimport numpy as np

Creation

It’s very simple!

You can create a Series or DataFrame from a list, tuple, NumPy array, or even a dictionary! Oh and of course from CSVs and databases.

From an array

# Seriesfuture_array1 = [1,2,3,4,5,6]array1 = np.array(future_array1)s = pd.Series(array1)

>>> s0 11 22 33 44 55 6dtype: int64

The print out you see above has two columns. The one on the left is the index and the one on the right is your data. This index looks like the indexes we are used to when using lists, tuples, arrays, or any other iterable. We will see soon in pandas we can change it to whatever we like!

# DataFramefuture_array2 = [2,4,6,8,10,12]array2 = np.array(future_array2)df = pd.DataFrame([future_array1, future_array2])

>>> df0 1 2 3 4 50 1 2 3 4 5 61 2 4 6 8 10 12

The print out you see above has a ton of numbers. The first column on the left is the index. The top row is the columns names (for now 0…5). Again, we will see soon in pandas we can change it to whatever we like!

From a dictionary

The dictionary keys will become the index in a Series

# Seriesfuture_series = {0: 'A', 1: 'B', 2: 'C'}s = pd.Series(future_series)

>>> s0 A1 B2 Cdtype: object

It works a bit differently in a DataFrame — the keys become the column names

# DataFramedict = {'Normal': ['A', 'B', 'C'], 'Reverse': ['Z', 'Y', 'X']}df = pd.DataFrame(dict)

>>> dfNormal Reverse0 A Z1 B Y2 C X

Upload data

Pandas has many ways to upload data, but let’s focus on the standard csv format.

uploaded_data = pd.read_csv("filename.csv", index_col=0)

The keyword argument, index_col, is where you can specify which column in your CSV should be the index in the DataFrame. For more details on the read_csv function, go here.

I love that the pandas library only requires 1 line to import data from a CSV. Who else is over copying and pasting the same lines of code from the csv library? ;)

Use the Index

Your days of text wrangling are over! No more weird list comprehensions or for loops with comments like “# extract this column during given period” or “# sorry for the mess”.

Here is an example DataFrame:

dates = pd.date_range("20160101", periods=6)data = np.random.random((6,3))column_names = ['Column1', 'Column2', 'Column3']df = pd.DataFrame(data, index=dates, columns=column_names)

>>> dfColumn1 Column2 Column3

2016-01-01 0.704351 0.151919 0.505881

2016-01-02 0.242099 0.887256 0.069512

2016-01-03 0.683565 0.305862 0.278066

2016-01-04 0.943801 0.388292 0.221318

2016-01-05 0.353116 0.418686 0.054011

2016-01-06 0.802379 0.720102 0.043310

Indexing a column

>>> df['Column2'] # use the column name's string

2016-01-01 0.151919

2016-01-02 0.887256

2016-01-03 0.305862

2016-01-04 0.388292

2016-01-05 0.418686

2016-01-06 0.720102

Freq: D, Name: Column2, dtype: float64

Indexing a row

>>> df[0:2] # use the standard indexing technique

        Column1   Column2   Column3

2016-01-01 0.704351 0.151919 0.505881

2016-01-02 0.242099 0.887256 0.069512

>>> df['20160101':'20160102'] # use the index's strings

        Column1   Column2   Column3

2016-01-01 0.704351 0.151919 0.505881

2016-01-02 0.242099 0.887256 0.069512

Indexing multiple axes — names

>>> df.loc['20160101':'20160102',['Column1','Column3']]

        Column1   Column3

2016-01-01 0.704351 0.505881

2016-01-02 0.242099 0.069512

Indexing multiple axes — numbers

>>> df.iloc[3:5, 0:2]

        Column1   Column2

2016-01-04 0.943801 0.388292

2016-01-05 0.353116 0.418686

View Your Data

Quickly check the top and bottom rows:

>>> df.head(2) # first 2 rows

        Column1   Column2   Column3

2016-01-01 0.704351 0.151919 0.505881

2016-01-02 0.242099 0.887256 0.069512

>>> df.tail(2) # last 2 rows

        Column1   Column2   Column3

2016-01-05 0.353116 0.418686 0.054011

2016-01-06 0.802379 0.720102 0.043310

View summary statistics before you dash off for a meeting:

>>> df.describe()

   Column1   Column2   Column3

count 6.000000 6.000000 6.000000

mean 0.621552 0.478686 0.195350

std 0.269550 0.273359 0.180485

min 0.242099 0.151919 0.043310

25% 0.435728 0.326470 0.057887

50% 0.693958 0.403489 0.145415

75% 0.777872 0.644748 0.263879

max 0.943801 0.887256 0.505881

Control Your Data

Pandas brings the flexibility of SQL into Python.

Sort

>>> df.sort_index(axis=0, ascending=False) # sort using the index

         Column1   Column2   Column3

2016-01-06 0.802379 0.720102 0.043310

2016-01-05 0.353116 0.418686 0.054011

2016-01-04 0.943801 0.388292 0.221318

2016-01-03 0.683565 0.305862 0.278066

2016-01-02 0.242099 0.887256 0.069512

2016-01-01 0.704351 0.151919 0.505881

>>> df.sort_values(by='Column2') # sort using a column

        Column1   Column2   Column3

2016-01-01 0.704351 0.151919 0.505881

2016-01-03 0.683565 0.305862 0.278066

2016-01-04 0.943801 0.388292 0.221318

2016-01-05 0.353116 0.418686 0.054011

2016-01-06 0.802379 0.720102 0.043310

2016-01-02 0.242099 0.887256 0.069512

Join

Here are new example DataFrames:

dates1 = pd.date_range("20160101", periods=6)data1 = np.random.random((6,2))column_names1 = ['ColumnA', 'ColumnB']

dates2 = pd.date_range("20160101", periods=7)data2 = np.random.random((7,2))column_names2 = ['ColumnC', 'ColumnD']

df1 = pd.DataFrame(data1, index=dates1, columns=column_names1)df2 = pd.DataFrame(data2, index=dates2, columns=column_names2)

>>> df1.join(df2) # joins on the index

        ColumnA   ColumnB   ColumnC   ColumnD

2016-01-01 0.128655 0.181495 0.574188 0.628584

2016-01-02 0.278669 0.810805 0.634820 0.545531

2016-01-03 0.489763 0.397794 0.169862 0.300666

2016-01-04 0.911465 0.903353 0.058488 0.911165

2016-01-05 0.094284 0.890642 0.282264 0.568099

2016-01-06 0.512656 0.735082 0.141056 0.698386

If you want to join on a column other than the index, check out the merge method.

Group by

df3 = df1.join(df2)

# add a column to df to group ondf3['ProfitLoss'] = pd.Series(['Profit', 'Loss', 'Profit', 'Profit', 'Profit', 'Loss'], index=dates)

>>> df3.groupby('ProfitLoss').mean()

        ColumnA   ColumnB   ColumnC   ColumnD

ProfitLoss

Loss 0.403947 0.759588 0.272969 0.305868

Profit 0.576668 0.477050 0.359661 0.406070

Accessing Attributes

Notice how I was able to just add in a column using a key/value notation in the code above? Pandas allows you to add new data with ease. But it also allows you to access the core attributes of your data structures.

Access the Index

>>> df3.index

DatetimeIndex(['2016-01-01', '2016-01-02', '2016-01-03', '2016-01-04','2016-01-05', '2016-01-06'],dtype='datetime64[ns]', freq='D')

Access the Values

>>> df3.values

array([[0.441513594483238, 0.974419927787583, 0.20896018007846018,

0.45913058454344435, 'Profit'], ...

[0.6980963896232228, 0.7005669323477245, 0.09231336594380268,

0.13264595083739117, 'Loss']], dtype=object)

Access the Columns

>>> df3.columns

Index([u'ColumnA', u'ColumnB', u'ColumnC', u'ColumnD', u'ProfitLoss'], dtype='object')

I’m providing here a link to download my pandas walkthrough using a Jupyter Notebook!

Never used Jupyter notebooks before? Visit their website here.

Overall, if you have a dataset you want to manipulate but don’t want to go to the hassle of hauling it all into SQL, I recommend searching for a pandas solution before anything else!

Applications

Let’s look at a scenario. Say you wanted to keep an eye on Bitcoin but don’t want to invest too much time in building out an infrastructure. You can use pandas to keep it simple.

You’ll need a Quandl account and the python Quandl library.

pip install quandl

Let’s code:

import quandl

# set up the Quandl connectionapi_key = 'GETYOURAPIKEY'quandl.ApiConfig.api_key = api_keyquandl_code = "BITSTAMP/USD"

# get the data from the APIbitcoin_data = quandl.get(quandl_code, start_date="2017-01-01", end_date="2018-01-17", returns="numpy")

# set up the data in pandasdf = pd.DataFrame(data=bitcoin_data, columns=['Date', 'High', 'Low', 'Last', 'Bid', 'Ask', 'Volume', 'VWAP'])

# make the 'Date' column the indexdf.set_index('Date', inplace=True)

# find a rolling 30 day averagedf['RollingMean'] = df['Last'].rolling(window=30).mean().shift(1)

# label when the last price is less than L30D averagedf['Buy'] = df['Last'] < df['RollingMean']

# create a strategic trading DataFrametrading_info = df.loc[:,['Last', 'RollingMean', 'Buy']]

>>> trading_info.tail(10) # lets look at last 10 days

            Last   RollingMean    Buy

Date

2018-01-08 16173.98 15693.421333 False

2018-01-09 15000.00 15704.147667 True

2018-01-10 14397.30 15716.680333 True

2018-01-11 14900.00 15706.590333 True

2018-01-12 13220.00 15655.209333 True

2018-01-13 13829.29 15539.209333 True

2018-01-14 14189.66 15458.548000 True

2018-01-15 13648.00 15384.760000 True

2018-01-16 13581.66 15258.109000 True

2018-01-17 11378.66 15070.668667 True

This is the power of pandas with real life data! However, what if we wanted to view the data shown above in a graph? That’s possible, check out my next article on Matplotlib.

Thanks for reading! If you have questions feel free to comment & I will try to get back to you.

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